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Abstract Human action understanding (HAU) is a broad topic that involves specific tasks, such as action localisation, recognition, and assessment. However, most popular HAU datasets are bound to one task based on particular actions. Combining different but relevant HAU tasks to establish a unified action understanding system is challenging due to the disparate actions across datasets. A large‐scale and comprehensive benchmark, namely SkatingVerse is constructed for action recognition, segmentation, proposal, and assessment. SkatingVerse focus on fine‐grained sport action, hence figure skating is chosen as the task object, which eliminates the biases of the object, scene, and space that exist in most previous datasets. In addition, skating actions have inherent complexity and similarity, which is an enormous challenge for current algorithms. A total of 1687 official figure skating competition videos was collected with a total of 184.4 h, exceeding four times over other datasets with a similar topic. SkatingVerse enables to formulate a unified task to output fine‐grained human action classification and assessment results from a raw figure skating competition video. In addition, SkatingVerse can facilitate the study of HAU foundation model due to its large scale and abundant categories. Moreover, image modality is incorporated for human pose estimation task into SkatingVerse. Extensive experimental results show that (1) SkatingVerse significantly helps the training and evaluation of HAU methods, (2) the performance of existing HAU methods has much room to improve, and SkatingVerse helps to reduce such gaps, and (3) unifying relevant tasks in HAU through a uniform dataset can facilitate more practical applications. SkatingVerse will be publicly available to facilitate further studies on relevant problems.

Abstract Fractional quantum Hall (FQH) states are exotic quantum many-body phases whose elementary charged excitations are anyons obeying fractional braiding statistics. While most FQH states are believed to have Abelian anyons, the Moore–Read type states with even denominators – appearing at half filling of a Landau level (LL) – are predicted to possess non-Abelian excitations with appealing potential in topological quantum computation. These states, however, depend sensitively on the orbital contents of the single-particle LL wavefunctions and the LL mixing. Here we report magnetotransport measurements on Bernal-stacked trilayer graphene, whose multiband structure facilitates interlaced LL mixing, which can be controlled by external magnetic and displacement fields. We observe robust FQH states including even-denominator ones at filling factors ν = − 9/2, − 3/2, 3/2 and 9/2. In addition, we fine-tune the LL mixing and crossings to drive quantum phase transitions of these half-filling states and neighbouring odd-denominator ones, exhibiting related emerging and waning behaviour.

Bladder cancer (BC) is the tenth most common malignancy globally. Urothelial carcinoma (UC) is a major type of BC, and advanced UC (aUC) is associated with poor clinical outcomes and limited survival rates. Current options for aUC treatment mainly include chemotherapy and immunotherapy. These options have moderate efficacy and modest impact on overall survival and thus highlight the need for novel therapeutic approaches. aUC patients harbor a high tumor mutation burden and abundant molecular alterations, which are the basis for targeted therapies. Erdafitinib is currently the only Food and Drug Administration (FDA)-approved targeted therapy for aUC. Many potential targeted therapeutics aiming at other molecular alterations are under investigation. This review summarizes the current understanding of molecular alterations associated with aUC targeted therapy. It also comprehensively discusses the related interventions for treatment in clinical research and the potential of using novel targeted drugs in combination therapy.

The design of universal segments and deviation control of segment assembly are essential for robust and low-risk tunnel construction. A building information modeling (BIM)-based framework was proposed for parametric modeling, automatic assembly, and deviation control of universal segments. First, segment models of different levels of detail (LoDs) were built based on BIM visual programming language (VPL) for different project life cycles. Then, the geometric constraints, requirements, and procedures for parametric segment assembly were distilled to develop a program that combines a novel typesetting algorithm with a 3D path replanning algorithm. Typesetting is implemented by introducing a point indication matrix, characterizing segments by sides, and manipulating geometries in a VPL. Simultaneously, 3D path replanning, with non-uniform rational B-splines (NURBS) and arcs as basic shapes, was used to resolve unacceptable deviation situations after typesetting. Finally, the proposed framework was validated on a water diversion line and was found to be more effective and accurate than the previous method.

ABSTRACT Introduction Patients with terminal‐stage cancer frequently experience multiple symptoms simultaneously. Little is known about how core symptom clusters differ in advanced‐cancer patients with different survival expectancies receiving hospice care. To identify the core symptom clusters of hospice‐care cancer patients with different survival expectancies and compare the features of their symptom networks. Methods In this retrospective study, secondary data analysis was conducted. Records of 6946 patients with advanced cancer who received home‐based hospice care service in a hospice center from April 2001 to December 2020 were collected and analyzed using network analysis. Results This analysis included 6946 patients with advanced cancer receiving hospice care. In patients with survival expectancies of 0–6 months, loss of appetite was identified as the core symptom (rs = 4.03, rb = 5.21, rc = 2.63), and five symptom clusters were identified. Malnutrition was the core symptom in patients with survival expectancies of 6–12 months (rs = 2.83, rb = 2.43, rc = 0.93), and nine symptom clusters were identified. Wasting syndrome was the core symptom cluster in two groups. The network density of symptoms in patients with

Urinary tumors pose a significant health threat because of their high prevalence and recurrence rates. Despite the availability of various treatment options, many patients poorly respond to traditional therapies, highlighting the urgent need for alternative approaches. Oncolytic viruses are promising therapeutic agents. These viruses exploit the unique characteristics of cancer cells to specifically target and destroy them, thereby triggering potent antitumor immune responses. This review delves into recent advancements and future prospects of oncolytic viruses, focusing on their application in renal, bladder, and prostate cancers. By discussing practical implications and the potential of different viruses, including the cowpox virus, adenovirus, measles virus, coxsackievirus, and reovirus, we pave the way for further exploration and refinement of this exciting field.

Abstract Background In the wake of China’s relaxed zero-COVID policy, there was a surge in coronavirus disease 2019 (COVID-19) infections. This study aimed to examine the infection status and health service utilization among Beijing residents during a widespread outbreak, and to explore the factors that affected utilization of health services due to COVID-19. Methods A cross-sectional survey was conducted among Beijing residents from 13 January to 13 February 2023, collecting information on socio-demographic characteristics, health behaviours, COVID-19 infection status, utilization of health services and depressive symptoms. Multivariate Tobit regression was used for data analysis. Results Among the 53 924 participants, 14.7% were older than 60 years, 63.7% were female and 84.8% were married. In total, 44 992 of the 53 924 individuals surveyed (83.4%) contracted COVID-19 during 2020–2023, and 25.2% (13 587) sought corresponding health services. The majority of individuals (85.6%) chose in-person healthcare, while 14.4% chose internet-based healthcare. Among those who chose in-person healthcare, 58.6% preferred primary healthcare institutions and 41.5% were very satisfied with the treatment. Factors affecting health service utilization include being female (β = −0.15, P

Objective To analyze the cost and effectiveness of different HIV screening strategies based on multi-center HIV residual risk study, so as to provide reference for blood centers to adopt appropriate HIV testing strategies. Methods According to the HIV screening and confirmation of blood donors in three blood centers in Anhui Province, the residual risk of different HIV screening strategies was estimated. A decision tree model was established to analyze the cost-effectiveness differences of three different screening strategies under current domestic policies. Results The residual risk of anti-HIV-1 +2 ELISA, HIV Ag/Ab1+2 ELISA and ELISA+NAT were 1.17×10-6,0.84×10-6 and 0.59×10-6, respectively. According to decision tree model analysis, HIV Ag/Ab1+2 ELISA had a cost-effectiveness advantage over anti-HIV 1+2 ELISA when there was no NAT, but the advantage of HIV Ag/Ab1+2 ELISA disappeared when there was one NAT. The cost of HIV reagents, the cost of HIV treatment and the cost of false positive discarding were sensitive factors of the model. Conclusion In this area, one anti-HIV 1+2 ELISA combined with one NAT has a cost-effectiveness advantage. Blood centers need to confirm and evaluate the ELISA reagents used before conducting HIV screening. Under the premise of ensuring sensitivity, reagent cost and reagent false positive rate are the key factors.

Post-transplant diabetes mellitus (PTDM) increases the risk of graft failure and death in liver transplant (LT) recipients. Experimental studies have indicated that enteric dysbiosis mediated by immunosuppressive tacrolimus (TAC) could contribute to glucose disorders, but no data on human recipients with PTDM have been reported. Here, by combining high-throughput shotgun metagenomics sequencing and metabolomics profiling, we characterized the intestinal microbiome (IM) in LT recipient cohort with or without PTDM and deciphered the potential relationship among IM, TAC dosage, and diabetes. By comparing with both non-PTDM and classical type 2 diabetes mellitus (T2DM), we identified microbial signatures of PTDM, which was characterized by the enriched Proteobacteria and decreased Bacteroidetes. Additionally, the altered microbes, as well as the microbial metabolomics, correlated with the dosage of TAC. Specifically, the levels of beneficial microbes associated with PTDM were lowered in recipients with the high TAC trough concentrations (> 5 ng∙mL–1) than those with low ones (< 5 ng∙mL–1), which was accompanied by reduced faecal metabolites involved in the biosynthesis of α-linolenic acid and arachidonic acid-lowering factors of developing T2DM. Moreover, these microbial signatures linked with the extent of glucose disorders in LT recipients. In summary, the faecal microbiome and metabolome differed between PTDM and non-PTDM patients, which were linked with TAC dosage. This study was the first to explore taxonomic alterations and bacterial gene functions to better understand the contribution of the IM to PTDM.

Biological composites such as bone, nacre, and teeth show excellent mechanical efficiency because of the incorporation of biominerals into the organic matrix at the nanoscale, leading to hierarchical composite structures. Adding a large volume of ceramic nanoparticles into an organic molecular network uniformly has been a challenge in engineering applications. However, in natural organisms, biominerals grow inside organic fibers, such as chitin and collagen, forming perfect ceramic/polymer composites spontaneously via biomineralization processes. Inspired from these processes, the in situ growth of calcium carbonate nanoparticles inside the chitosan network to form ceramic composites was proposed in the current work. The crystal growth of CaCO3 nanoparticles in the chitosan matrix as a function of time was investigated. A weight percentage of ~35 wt% CaCO3 composite was realized, resembling the high weight percentage of mineral phase in bones. Scanning and transmission electron microscopy indicated the integration of CaCO3 nanocrystals with chitosan macromolecules. By growing CaCO3 minerals inside the chitosan matrix, the elastic modulus and tensile strength increases by ~110% and ~90%, respectively. The in situ crystal growth strategy was also demonstrated in organic frameworks prepared via 3D printing, indicating the potential of fabricating ceramic/polymer composites with complicated structures, and further applications in tissue engineering.

Abstract Background Gut microbiota imbalances have been suggested as a contributing factor to atrial fibrillation (AF), but the causal relationship is not fully understood. Objectives To explore the causal relationships between the gut microbiota and AF using Mendelian randomization (MR) analysis. Methods Summary statistics were from genome-wide association studies (GWAS) of 207 gut microbial taxa (5 phyla, 10 classes, 13 orders, 26 families, 48 genera, and 105 species) (the Dutch Microbiome Project) and two large meta-GWASs of AF. The significant results were validated in FinnGen cohort and over 430,000 UK Biobank participants. Mediation MR analyses were conducted for AF risk factors, including type 2 diabetes, coronary artery disease (CAD), body mass index (BMI), blood lipids, blood pressure, and obstructive sleep apnea, to explore the potential mediation effect of these risk factors in between the gut microbiota and AF. Results Two microbial taxa causally associated with AF: species Eubacterium ramulus (odds ratio [OR] 1.08, 95% confidence interval [CI] 1.04–1.12, P = 0.0001, false discovery rate (FDR) adjusted p-value = 0.023) and genus Holdemania (OR 1.15, 95% CI 1.07–1.25, P = 0.0004, FDR adjusted p-value = 0.042). Genus Holdemania was associated with incident AF risk in the UK Biobank. The proportion of mediation effect of species Eubacterium ramulus via CAD was 8.05% (95% CI 1.73% − 14.95%, P = 0.008), while the proportion of genus Holdemania on AF via BMI was 12.01% (95% CI 5.17% − 19.39%, P = 0.0005). Conclusions This study provided genetic evidence to support a potential causal mechanism between gut microbiota and AF and suggested the mediation role of AF risk factors.

Abstract Aims Left ventricular (LV) remodelling after acute myocardial infarction (AMI) is associated with heart failure and increased mortality. There was no consensus on the definition of LV remodelling, and the prognostic value of LV remodelling with different definitions has not been compared. We aimed to find the optimal definition and develop a prediction nomogram as well as online calculator that can identify patients at risk of LV remodelling. Methods and results This prospective, observational study included 829 AMI patients undergoing percutaneous coronary intervention from January 2015 to January 2020. Echocardiography was performed within the 48 h of admission and at 6 months after infarction to evaluate LV remodelling, defined as a 20% increase in LV end‐diastolic volume (LVEDV), a 15% increase in LV end‐systolic volume (LVESV), or LV ejection fraction (LVEF)

Abstract Background Schatzker IV-C is a high-energy tibial plateau fracture often accompanied by lateral meniscus injuries. While imaging examinations are routine preoperative measurements, the correlation between CT imaging shift parameters of the lateral plateau and lateral meniscal injury in Schatzker IV-C fractures remains uncovered. Methods This retrospective study enrolled a total of 60 patients with Schatzker IV-C tibial plateau fractures at the First People’s Hospital of Hefei. Prior to surgery, CT imaging was used to measure the numerical values of lateral plateau depression (LPD) and lateral plateau widening (LPW). The degree of lateral meniscus injury was confirmed based on intraoperative direct vision, with patients being classified into meniscus injury and non-meniscus injury groups. Dichotomous logistic regression was employed to evaluate the correlation between LPD, LPW, and lateral meniscus injury, while the optimal cut-off points for predicting lateral meniscal injury with LPD and LPW were determined using receiver operator characteristic (ROC) curves. Results The meniscus injury group exhibited a mean LPD of 15.3 ± 3.5 mm, which was significantly higher than the non-meniscus injury group’s mean LPD of 8.4 ± 3.4 mm (P

Abstract Background Endobronchial ultrasonography-guided transbronchial needle aspiration biopsy (EBUS-TBNA) has been used for more than 10 years in China. Its clinical application and diagnostic value in different diseases with large sample was lack of report. Methods A retrospective analysis was performed about the application and diagnostic value of EBUS-TBNA in different disease of patients in Respiratory Intervention Center of Guangzhou Institute of Respiratory Health from January 2012 to July 2020. Results A total 5758 patients were included with 182 patients excluded for lack of information. Finally, data of 5576 patients (3798 males and 1778 females) were analyzed. For anesthetize, most patients were undergoing general anesthesia of intravenous with spontaneous breathing (69.4%), followed by general anesthesia of intravenous and inhalation with tracheal intubation and mechanical ventilation (17.9%) and conscious sedation and analgesia (12.8%). Lymph nodes were the main sites of biopsy obtained (76.4%). Tumors accounted for the highest proportion of disease (66.4%), followed by infection diseases (9.9%), sarcoidosis (3.9%), lymphoma (1.1%), and others (18.7%). The sensitivity of EBUS-TBNA for diagnosis of tumor was 89.7%, and 40.8% for infection diseases. There were significant differences in the puncture site and proportions of diseases between male and females (both p

Bacillus Calmette–Guérin (BCG) immunotherapy has been a cornerstone treatment for non-muscle-invasive bladder cancer for decades and still faces challenges, such as severe immune adverse reactions, which reduce its use as a first-line treatment. This review examines BCG therapy’s history, mechanisms, and current status, highlighting how nanotechnology and bioengineering are revitalizing its application. We discuss novel nanocarrier systems aimed at enhancing BCG’s efficacy while mitigating specific side effects. These approaches promise improved tumor targeting, better drug loading, and an enhanced stimulation of anti-tumor immune responses. Key strategies involve using materials such as liposomes, polymers, and magnetic particles to encapsulate BCG or functional BCG cell wall components. Additionally, co-delivering BCG with chemotherapeutics enhances drug targeting and tumor-killing effects while reducing drug toxicity, with some studies even achieving synergistic effects. While most studies remain experimental, this research direction offers hope for overcoming BCG’s limitations and advancing bladder cancer immunotherapy. Further elucidation of BCG’s mechanisms and rigorous safety evaluations of new delivery systems will be crucial for translating these innovations into clinical practice.

Abstract Background Evidence links gamma‐glutamyl transferase (GGT) to mortality in the general population. However, the relationship of GGT with all‐cause and cause‐specific mortality risk has been little explored in type 2 diabetes mellitus (T2DM) patients. Methods We recruited 20 340 community‐dwelling T2DM patients between 2013 and 2014 in Jiangsu, China. Cox regression models were used to assess associations of GGT with all‐cause and specific‐cause mortality. Restricted cubic splines were used to analyze dose–response relationships between GGT and mortality. Stratified analysis was conducted to examine potential interaction effects by age, sex, smoking status, body mass index (BMI), diabetes duration, and dyslipidemia. Results During a median follow‐up period of 7.04 years (interquartile range: 6.98–7.08), 2728 deaths occurred, including 902 (33.09%) due to cardiovascular disease (CVD), and 754 (27.58%) due to cancer. GGT concentrations were positively associated with all‐cause, CVD, and cancer mortality. Multivariable hazard ratios (HRs) for the highest (Q5) vs. the lowest quintile (Q1) were 1.63 (95% confidence intervals [CI]: 1.44–1.84) for all‐cause mortality, 1.87 (95% CI: 1.49–2.35) for CVD mortality, and 1.43 (95% CI: 1.13–1.81) for cancer mortality. Effect modification by BMI and dyslipidemia was observed for all‐cause mortality (both p for interaction

Summary Background To compare the ability of the Cox regression and machine learning algorithms to predict the survival of patients with Anaplastic thyroid carcinoma (ATC). Methods Patients diagnosed with ATC were extracted from the Surveillance, Epidemiology, and End Results database. The outcomes were overall survival (OS) and cancer-specific survival (CSS), divided into: (1) binary data: survival or not at 6 months and 1 year; (2): time-to-event data. The Cox regression method and machine learnings were used to construct models. Model performance was evaluated using the concordance index (C-index), brier score and calibration curves. The SHapley Additive exPlanations (SHAP) method was deployed to interpret the results of machine learning models. Results For binary outcomes, the Logistic algorithm performed best in the prediction of 6-month OS, 12-month OS, 6-month CSS, and 12-month CSS (C-index = 0.790, 0.811, 0.775, 0.768). For time-event outcomes, traditional Cox regression exhibited good performances (OS: C-index = 0.713; CSS: C-index = 0.712). The DeepSurv algorithm performed the best in the training set (OS: C-index = 0.945; CSS: C-index = 0.834) but performs poorly in the verification set (OS: C-index = 0.658; CSS: C-index = 0.676). The brier score and calibration curve showed favorable consistency between the predicted and actual survival. The SHAP values was deployed to explain the best machine learning prediction model. Conclusions Cox regression and machine learning models combined with the SHAP method can predict the prognosis of ATC patients in clinical practice. However, due to the small sample size and lack of external validation, our findings should be interpreted with caution.

BackgroundThe Chinese government has ended the “dynamic zero-COVID” policy, and residents are now living together with the SARS-CoV-2 virus. Only a limited number of studies have investigated the specific content and structure of COVID-19-related risk perceptions, as well as their underlying determinants. This study measured the residents’ risk perception of COVID-19 and analyzed the predictors of RP.MethodsWe conducted a comprehensive questionnaire-based survey among residents mostly in Beijing, using a specially designed scale consisting of 11 items to accurately measure COVID-19 risk perceptions. We then utilized multiple linear regression analysis to investigate the factors associated with risk perceptions.ResultsA total of 60,039 residents participated in the survey. Our study reveals that COVID-19-related worries are significantly influenced by other dimensions of RP (p

BackgroundWhile Type 2 Diabetes Mellitus (T2DM) prevalence is increasing among younger individuals, few studies have examined how age at T2DM diagnosis relates to dementia risk in diabetic populations. We aimed to investigate the association between age at T2DM diagnosis and subsequent dementia risk, and to determine whether obesity moderates this relationship.MethodsWe conducted a prospective cohort study using data from the Health and Retirement Study (2002-2016) matched with its 2003 Diabetes Mail-Out Survey. The study included 1,213 dementia-free adults aged ≥50 with diagnosed T2DM. Primary exposures were age at T2DM diagnosis (categorized as ResultsOver a median follow-up of 10 (interquartile range, 6-14) years, 216 (17.8%) participants developed dementia. Compared to participants diagnosed with T2DM at age ≥70 years, those diagnosed at younger ages had increased dementia risk: HR 1.70 (95% CI, 1.03-2.80) for 60-69 years, 1.72 (95% CI, 1.06-2.79) for 50-59 years, and 1.90 (95% CI, 1.14-3.18) for ConclusionsYounger age at diagnosis of T2DM was significantly associated with a higher risk of dementia, particularly among individuals with obesity. Interventions specifically targeting obesity may be more effective in preventing dementia for adults with a younger onset of T2DM.

Background: Body mass index (BMI) is known to affect the outcomes of spirometry indices. However, its association with spirometry indices in COPD and asthma is less studied. We aimed to explore the impact of BMI on these patients.Methods: Patients with COPD or asthma who completed bronchodilator tests (BDTs) between 2017 and 2021 were reviewed. Spirometry indices were compared among patients with COPD or asthma that were subclassified as underweight (BMI< 18.5 kg/m2), normal weight (≥18.5 to < 25), overweight (≥ 25 to < 30), and obesity (≥ 30). Results.Results: Analysis was conducted on 3891 COPD patients (age:66.5 ± 7.8 years) and 1208 asthma patients (age:59.7 ± 7.5 years). COPD patients classified as underweight demonstrated significantly lower values of pre-and post FEV1 (L, %), pre-and post FVC (L, %), and pre- and post-FEV1/FVC (all p < 0.05). In contrast, COPD patients who were overweight or obese exhibited higher values for pre-and post FEV1 (L, %), and pre and post FEV1/FVC (all p < 0.05). Within the cohort of asthma patients, those underweight had lower pre-and post FEV1 (L, %), pre and post FVC (L, %), pre and post FEV1/FVC %. Obese asthma patients displayed higher pre and post FEV1/FVC (all p < 0.05).Conclusion: Significant BMI category differences in spirometry indices can be seen in patients with COPD or asthma. Both underweight and obesity could affect the diagnosis and severity of these diseases. Recognizing these effects is essential to better management and diagnosis of these patients.

The data-driven Intelligent Transportation System (ITS) provides great support to travel decisions and system management but inevitably encounters the issue of data missing in monitoring systems. Hence, network-wide traffic state prediction and imputation is critical to recognizing the system level state of a transportation network. Abundant research works have adopted various approaches for traffic prediction and imputation. However, previous methods ignore the reliability analysis of the predicted/imputed traffic information. Thus, this study originally proposes an attentive graph neural process (AGNP) method for network-level short-term traffic speed prediction and imputation, simultaneously considering reliability. Firstly, the Gaussian process (GP) is used to model the observed traffic speed state. Such a stochastic process is further learned by the proposed AGNP method, which is utilized for inferring the congestion state on the remaining unobserved road segments. Data from a transportation network in Anhui Province, China, is used to conduct three experiments with increasing missing data ratio for model testing. Based on comparisons against other machine learning models, the results show that the proposed AGNP model can impute traffic networks and predict traffic speed with high-level performance. With the probabilistic confidence provided by the AGNP, reliability analysis is conducted both numerically and visually to show that the predicted distributions are beneficial to guide traffic control strategies and travel plans.

The evolving economic and technological landscape has brought about significant changes in travel behaviors and traffic patterns. These changes have led to the emergence of complex, multi-modal travel demands that interact with transportation networks, posing new challenges in transportation analysis and control. The primary objective of this study is to address these challenges by improving transportation modeling and data completeness using advanced modeling tools and transportation big data. We propose a dual-driven simulation model that integrates transportation simulation and big data. The approach begins by utilizing initial Location-Based Services (LBS) data to establish a mesoscopic multi-modal simulation model, which is then calibrated. This calibrated model is then employed to complete the missing trajectories of the LBS data. The innovative aspect of this dual-driven simulation model lies in its novel approach to constructing transportation models and completing LBS data, thereby enhancing both the simulation accuracy and the results of missing path completion. We conduct tests using the urban area of Hangzhou as an example, and the results show that the Normalized Root Mean Square Error (NRMSE) between the average link speeds in the simulation model and in real world observation is reduced to 24.1%. In the LBS data completion process, our proposed method achieves a travel mode identification accuracy of 95.3% for private car travel. Compared to the two baseline methods, the average accuracy of completed trajectories increases by 6.31% and 2.46%, respectively.

Objective We investigated the predictive value of a logistic model utilizing hematological parameters in diagnosing occupational lead poisoning. Methods This retrospective study (September 2020–December 2022) included patients with occupational lead poisoning. Differences in hematological parameters were compared between individuals with occupational blood lead poisoning and healthy individuals. We used logistic regression analysis to develop a diagnostic prediction model for occupational blood lead poisoning. Receiver operating characteristic (ROC) curves and corresponding area under the ROC curve values were used to assess the diagnostic value of hematological parameters and logistic models. Results Compared with controls, several indicators were significantly higher in the group with blood lead poisoning, but others were significantly lower. Logistic regression analysis showed that the red blood cell distribution width coefficient of variation (RDW-CV), neutrophil/lymphocyte ratio (NLR), and percentage of small red blood cells (Micro%) were independent factors in diagnosing occupational blood lead poisoning. The logistic regression model constructed based on these three parameters had sensitivity 78.7% and specificity 83.8% for diagnosing occupational lead poisoning. Conclusion We identified RDW-CV, NLR, and Micro% as independent predictors in the diagnosis of occupational lead poisoning. A logistic regression model that includes these may contribute to better detection of occupational lead poisoning.

Background Low vitamin D status has been linked to an increased risk for various inflammatory diseases. Conflicting results have been reported regarding chronic obstructive pulmonary disease (COPD). This study aims to investigate the associations of serum 25-hydroxyvitamin D (25(OH)D) concentrations with COPD risk and survival.Methods We included 403 648 participants with serum 25(OH)D measurements and free of COPD at baseline from UK Biobank. Follow-up was until 30 September 2021. Multivariable-adjusted cox regression models were applied to estimate HRs and 95% CIs for the associations of season-standardised 25(OH)D concentrations with COPD risk and survival. The restricted cubic splines were used to assess dose–response relationship. Kaplan-Meier estimation was used to create graphs of the survival curves.Results During a median follow-up of 12.3 (IQR: 11.4–13.2) years, 11 008 cases of COPD were recorded. We observed a non-linear inverse association between 25(OH)D concentrations and COPD risk. Compared with participants in the fourth quintile of 25(OH)D, those in the lowest quintile were associated with a 23% higher risk (HR, 1.23; 95% CI, 1.16 to 1.31). Stronger associations were observed for the risk in men and current smokers (Both p for interaction

Abstract Bladder cancer (BCa) is one of the most common malignancies worldwide. Although multiple efforts have been made, the 5‐year survival rate of patients with BCa remains unchanged in recent years. Overexpression of the epidermal growth factor receptor (EGFR) is found in ≈74% of BCa tissue specimens; however, current EGFR‐based targeted therapies show little benefit for BCa patients, as the EGFR downstream pathways appear to be circumvented by other receptor tyrosine kinases (RTKs). In this study, two natural products are identified, namely triptolide (TPL) and hesperidin (HSP), that target and inhibit the EGFR and its downstream PI3K/AKT pathway in BCa. To synergistically combine triptolide and hesperidin, a succinic acid linker was employed to conjugate them and formed an amphiphilic TPL‐HSP EGFR‐targeting prodrug (THE), which further self‐assembled to generate nanoparticles (THE NPs). These NPs allowed the EGFR‐targeted delivery of the triptolide and hesperidin, and simultaneous inhibition of the EGFR and PI3K/AKT both in vitro and in vivo. This study provides a promising EGFR‐targeted delivery approach with the dual inhibition of the EGFR and PI3K/AKT, while also exhibiting a high drug loading and low toxicity. Our formulation may be a suitable option to deliver natural products for BCa treatment by EGFR‐targeted therapy.

Abstract Psoriatic arthritis (PsA) is associated with psoriasis, featured by its irreversible joint symptoms. Despite the significant impact on the healthcare system, it is still challenging to leverage machine learning or statistical models to predict PsA and its progression, or analyze drug efficacy. With 3961 patients’ clinical records, we developed a machine learning model for PsA diagnosis and analysis of PsA progression risk, respectively. Furthermore, general additive models (GAMs) and the Kaplan–Meier (KM) method were applied to analyze the efficacy of various drugs on psoriasis treatment and inhibiting PsA progression. The independent experiment on the PsA prediction model demonstrates outstanding prediction performance with an AUC score of 0.87 and an AUPR score of 0.89, and the Jackknife validation test on the PsA progression prediction model also suggests the superior performance with an AUC score of 0.80 and an AUPR score of 0.83, respectively. We also identified that interleukin-17 inhibitors were the more effective drug for severe psoriasis compared to other drugs, and methotrexate had a lower effect in inhibiting PsA progression. The results demonstrate that machine learning and statistical approaches enable accurate early prediction of PsA and its progression, and analysis of drug efficacy.

Abstract Aims/Introduction To compare the application value of different machine learning (ML) algorithms for diabetes risk prediction. Materials and Methods This is a 3‐year retrospective cohort study with a total of 3,687 participants being included in the data analysis. Modeling variable screening and predictive model building were carried out using logistic regression (LR) analysis and 10‐fold cross‐validation, respectively. In total, six different ML algorithms, including random forests, light gradient boosting machine, extreme gradient boosting, adaptive boosting (AdaBoost), multi‐layer perceptrons and gaussian naive bayes were used for model construction. Model performance was mainly evaluated by the area under the receiver operating characteristic curve. The best performing ML model was selected for comparison with the traditional LR model and visualized using Shapley additive explanations. Results A total of eight risk factors most associated with the development of diabetes were identified by univariate and multivariate LR analysis, and they were visualized in the form of a nomogram. Among the six different ML models, the random forests model had the best predictive performance. After 10‐fold cross‐validation, its optimal model has an area under the receiver operating characteristic value of 0.855 (95% confidence interval [CI] 0.823–0.886) in the training set and 0.835 (95% CI 0.779–0.892) in the test set. In the traditional LR model, its area under the receiver operating characteristic value is 0.840 (95% CI 0.814–0.866) in the training set and 0.834 (95% CI 0.785–0.884) in the test set. Conclusions In the real‐world epidemiological research, the combination of traditional variable screening and ML algorithm to construct a diabetes risk prediction model has satisfactory clinical application value.

Abstract Immunotherapy has become the central pillar of cancer therapy. Immune checkpoint inhibitors (ICIs), a major category of tumor immunotherapy, reactivate preexisting anticancer immunity. Initially, ICIs were approved only for advanced and metastatic cancers in the salvage setting after or concurrent with chemotherapy at a response rate of around 20–30% with a few exceptions. With significant progress over the decade, advances in immunotherapy have led to numerous clinical trials investigating ICIs as neoadjuvant and/or adjuvant therapies for resectable solid tumors. The promising results of these trials have led to the United States Food and Drug Administration (FDA) approvals of ICIs as neoadjuvant or adjuvant therapies for non-small cell lung cancer, melanoma, triple-negative breast cancer, and bladder cancer, and the list continues to grow. This therapy represents a paradigm shift in cancer treatment, as many early-stage cancer patients could be cured with the introduction of immunotherapy in the early stages of cancer. Therefore, this topic became one of the main themes at the 2021 China Cancer Immunotherapy Workshop co-organized by the Chinese American Hematologist and Oncologist Network, the China National Medical Products Administration and the Tsinghua University School of Medicine. This review article summarizes the current landscape of ICI-based immunotherapy, emphasizing the new clinical developments of ICIs as curative neoadjuvant and adjuvant therapies for early-stage disease.

Abstract Background Symptom networks can provide empirical evidence for the development of personalized and precise symptom management strategies. However, few studies have explored the symptom networks of multidimensional symptom experiences in cancer survivors. The objectives of this study were to generate symptom networks of multidimensional symptom experiences in cancer survivors and explore the centrality indices and density in these symptom networks Methods Data from 1065 cancer survivors were obtained from the Shanghai CANcer Survivor (SCANS) Report. The MD Anderson Symptom Inventory was used to assess the prevalence and severity of 13 cancer‐related symptoms. We constructed contemporaneous networks with all 13 symptoms after controlling for covariates. Results Distress (rs = 9.18, rc = 0.06), sadness (rs = 9.05, rc = 0.06), and lack of appetite (rs = 9.04, rc = 0.06) had the largest values for strength and closeness. The density of the “less than 5 years” network was significantly different from that of the “5–10 years” and “over 10 years” networks (p

Objectives: Rabies is an acute lethal infectious disease caused by a lyssavirus infection. In 2018, the World Health Organization proposed a global strategic plan to end human rabies deaths by 2030. However, systematic studies on the global rabies disease burden and epidemiological trends are scarce. Methods: We extracted the disease burden and epidemiological data of rabies worldwide in the preceding 30 years from the Global Burden of Disease Study 2019 and performed a comprehensive analysis. Results: In 2019, the incident cases of rabies worldwide were 14,075.51 (95% uncertainty interval: 6124.33-21,618.11), and the number of deaths was 13,743.44 (95% uncertainty interval: 6019.13-17,938.53), both of which were lower than that in 1990. With the improvement of the sociodemographic index, the incident cases, the number of deaths, age-standardized incidence rate, age-standardized incidence death rate, and disability-adjusted life years of rabies all showed downward trends. Adolescents and adults aged

Background/Objective: Regular physical activity (PA) is beneficial to young people's health and development. In order to provide nationally representative and internationally comparable evidence on youth PA, China has participated in Global Matrix 2.0 and 3.0. The purpose of this study is to report the updated results of China's Report Card on PA for Children and Adolescents. Methods: The grades were assigned by results derived from the PA and Fitness in China––The Youth Study (PAFCTYS), conducted from October to December in 2020. The data from PAFCTYS 2020 included a nationally representative sample of Chinese school-aged children (n = 133,006, boys: 49.6%, aged 9–17 years). Self-report questionnaires were completed by the sampled students, their parents/guardians (n = 133,006), and physical education teachers (n = 1036) from each sampled school respectively. Results: The grades of China 2022 Report Card are Overall PA (C), Organized Sport Participation (F), Active Play (C-), Active Transportation (C), Sedentary behaviors (C), Physical Fitness (INC), Family and Peers (C-), School (D), Community and Environment (D-), and Government (D). Conclusion: Levels of PA among Chinese youth were low and most young people were below the recommended guidelines, although the grade of Overall PA has been improved since the modified benchmark. Prevalence of sedentary behaviors remained high. Interventions and policies at the community and environment level should be encouraged to promote PA and reduce sedentary behaviors. In addition, national policies on young people's PA should be advocated widely to ensure the policies can be transferred into action.

Abstract Background Age is an independent prognostic factor for small cell lung cancer (SCLC). We aimed to construct a nomogram survival prediction for elderly SCLC patients based on the Surveillance, Epidemiology, and End Results (SEER) database. Methods A total of 2851 elderly SCLC patients from the SEER database were selected as a primary cohort, which were randomly divided into a training cohort and an internal validation cohort. Additionally, 512 patients from two institutions in China were identified as an external validation cohort. We used univariate and multivariate to determine the independent prognostic factors and establish a nomogram to predict survival. The value of the nomogram was evaluated by calibration plots, concordance index (C-index) and decision curve analysis (DCA). Results Ten independent prognostic factors were determined and integrated into the nomogram. Calibration plots showed an ideal agreement between the nomogram predicted and actual observed probability of survival. The C-indexes of the training and validation groups for cancer-specific survival (CSS) (0.757 and 0.756, respectively) based on the nomogram were higher than those of the TNM staging system (0.631 and 0.638, respectively). Improved AUC value and DCA were also obtained in comparison with the TNM model. The risk stratification system can significantly distinguish individuals with different survival risks. Conclusion We constructed and externally validated a nomogram to predict survival for elderly patients with SCLC. Our novel nomogram outperforms the traditional TNM staging system and provides more accurate prediction for the prognosis of elderly SCLC patients.

Abstract Background Liver cancer is the fifth leading cause of cancer death worldwide, but early diagnosis and treatment of liver cancer remains a clinical challenge. How to screen and diagnose liver cancer early and prolong the survival rate is still the focus of researchers. Methods Cell experiments were used to detect the effect of WZ35 on the colony formation ability and proliferation activity of hepatoma cells, nude mouse experiment to observe the in vivo anticancer activity and toxic side effects of WZ35; metabolomics analysis, glucose metabolism experiment and Seahorse analysis of liver cancer cells treated with WZ35; cell experiments combined with bioinformatics analysis to explore the mechanism of WZ35-mediated metabolic reprogramming to exert anticancer activity; tissue microarray and case analysis to evaluate the clinical significance of biomarkers for early diagnosis, treatment and prognosis evaluation of liver cancer. Results WZ35 inhibited the proliferation activity of various cell lines of liver cancer, and showed good therapeutic effect in nude mice model of hepatocellular carcinoma without obvious toxic and side effects; WZ35 inhibited the absorption of glucose in hepatoma cells, and the drug effect glycolysis, phosphorylation and purine metabolism are relatively seriously damaged; WZ35 mainly inhibits YAP from entering the nucleus as a transcription factor activator by activating oxidative stress in liver cancer cells, reducing the transcription of GLUT1, and finally reducing its GLUT1. Tissue microarray and case analysis showed that GLUT1 and YAP were highly expressed and correlated in liver cancer patients, and were associated with poor patient prognosis. The GLUT1-YAP risk model had a high score in predicting prognosis. Conclusion The study confirms that WZ35 is a small molecule glycolysis inhibitor, and through its properties, it mediates metabolic reprogramming dominated by impaired glycolysis, oxidative phosphorylation and purine metabolism to inhibit the proliferation activity of liver cancer cells. Our findings present novel insights into the pathology of liver cancer and potential targets for new therapeutic strategies. GLUT1-YAP has important reference significance for predicting the stages of disease progression in liver cancer patients and have the potential to serve as novel biomarkers for the diagnosis and treatment of liver cancer.

A wind power curtailment consumption strategy using electric vehicles (EVs) based on individual differential evolution quantum particle swarm optimization algorithm (IDE-QPSO) is proposed, with the objective of reducing the system’s wind curtailment in order to further improve the wind power consumption rate while effectively reducing wind power output fluctuation and amplitude. EV aggregators act as charging tariff setters, releasing dynamic time-of-use tariffs (DTOU) for EV clusters to respond to based on wind curtailment data accounted for by the dispatch center. This method first establishes an electric vehicle charging load model based on the travel chain theory and residents’ travel rules, then establishes an EV users autonomous response model based on the sensitivity of electric vehicle users to the charging prices. Second, a multi-objective optimization function is established based on the aforementioned model, which integrates wind power curtailment consumption and minimizes wind power output fluctuation and amplitude, and it is solved using an improved quantum particle swarm optimization algorithm. Finally, adequate simulation experiments show that this strategy can effectively smooth out the fluctuation of wind power output and improve the wind power consumption rate.

BackgroundA shift in research interest from separate care problem to care problem clusters among caregivers of people living with dementia may contribute to a better understanding of dementia care. However, the care problems network among caregivers of people living with dementia are still unknown. This study aimed to identify care problem clusters and core care problems, and explore demographic variables associated with these care problem clusters among caregivers of people living with dementia.MethodsParticipants were recruited through memory clinics and WeChat groups. The principal component analysis was applied to identify care problem clusters. The network analysis was conducted to describe the relationships among care problems and clusters. Multiple linear models were used to explore the associated factors for the occurrence of the overall care problems and top three central care problem clusters.ResultsA total of 1,012 carer-patient pairs were included in the analysis. Nine care problem clusters were identified. In the entire care problem network, “deterioration in activities of daily living” was the most core care problem cluster across the three centrality indices, followed by “verbal and nonverbal aggression” and “loss of activities of daily living.” Variables including marital status, years of dementia diagnosis, number of dementia medication type, and caregiver’s educational attainment were associated with the prevalence of these three care problem clusters.ConclusionOur study suggests that there is a need to evaluate care problem clusters for the improvement of care problem management among people living with dementia. It is particularly important to include assessment and treatment of core care problem as an essential component of the dementia care.

.Since 2019, the Chinese central government has taken significant steps to centralize national purchasing power and has implemented a pooled procurement system. In this paper, we provide an in-depth analysis of China's National Volume-Based Procurement (NVBP) policy, which represents a unique approach to pooled procurement within the pharmaceutical sector. The primary objectives of the NVBP are to reduce drug prices, enhance access to affordable medications, and improve the overall functioning of the pharmaceutical industry in China. Our analysis delves into the key features of the NVBP, including its centralized procurement system, volume-based procurement approach, and the guaranteed procurement volumes allocated to winning bidders. We also address the challenges and implications associated with the NVBP, such as its impact on the pharmaceutical industry, the sustainability of price reductions, and the importance of striking a balance between price reduction and industry sustainability. Through a comparative analysis, we shed light on the distinct characteristics of China's approach to pooled procurement and its potential ramifications for healthcare policies and practices. By examining the NVBP within the broader context of China's evolving healthcare landscape, we aim to contribute to a deeper understanding of the implications and effectiveness of this unique policy initiative.

Objectives In December 2018, China launched national volume-based procurement (NVBP) to negotiate drug prices with manufacturers. Gefitinib was one of the 25 pilot drugs, which is used for treatment of non-small cell lung cancer. Lung cancer is the most common type of cancer in China and targeted drugs like gefitinib have been proven to provide clinical benefits to patients. This study aims to explore the impact of NVBP policy on the usage and expenditure of anticancer drugs.Methods Gefitinib and alternative drugs (icotinib and erlotinib) were used as objects of study. Quarterly data from the China Hospital Pharmaceutical Audit database in 9454 hospitals in China were used for analysis. Descriptive analysis was conducted using purchase volume and expenditure as variables. Interrupted time-series (ITS) analysis was applied to further analyse the effect of NVBP policy on the medicines under study.Results During the 12-month period before (2018Q2–2019Q1) and after (2019Q2–2020Q1) the NVBP policy, the total purchase volume of medicines rose from 4.48 million defined daily dose (DDD) to 7.02 million DDD, with an increase of 56.66%. Purchase volume of gefitinib and alternative drugs increased 100.61% and 14.88%, respectively. After the implementation of NVBP policy, procurement volume of alternative drugs decreased by 72 051 DDD (p value=0.044) and trend change decreased by 56 738 DDD (p value

Objective Investigating the associations of alanine aminotransferase (ALT) and aspartate aminotransferase (AST) with all-cause, cardiovascular disease (CVD) and cancer mortality in a large cohort of community-dwelling patients with type 2 diabetes mellitus (T2DM).Design Community-based prospective cohort study conducted between 2013 and 2014.Setting 44 selected townships in Changshu and Huai’an City, Jiangsu province, China.Participants 20340 participants with T2DM were recruited in Jiangsu province, China.Methods We use Cox proportional hazard models to estimate the HR and 95% CIs of associations of serum ALT and AST levels with all-cause and cause-specific mortality. Restricted cubic splines were used to explore the dose-response relationships between ALT and AST levels with mortality.Results ALT and AST levels were inversely associated with CVD mortality, compared with the lowest quintile (Q1), the multivariable HRs of the highest quintile (Q5) was 0.82 (95% CI: 0.66 to 1.01, p for trend=0.022) and 0.78 (95% CI: 0.63 to 0.96, p for trend=0.022), respectively. Furthermore, the HRs for ALT levels in all-cause mortality were 0.90 (95% CI: 0.79 to 1.01, p for trend=0.018), and the HRs for AST levels in cancer mortality were 1.29 (95% CI: 1.02 to 1.63, p for trend=0.023). Stronger inverse effects of ALT and AST levels on all-cause mortality were observed in the older subgroup and in those with dyslipidaemia (all p for interaction

Consensus is lacking regarding whether high-intensity interval training (HIIT) effectively improves VO2max (VO2peak) in elite athletes (Athlete must be involved in regular competition at the national level). This meta-analysis compared the effects of HIIT and conventional training methods (continuous training, repeated-sprint training, high volume low-intensity training, high-intensity continuous running, sprint-interval training, moderate-intensity continuous training)on VO2max in elite athletes. Nine studies were included, comprising 176 elite athletes (80 female). Compared to that with conventional training, VO2max was significantly increased after HIIT (overall: 0.58 [0.30, 0.87], I2 = 0.49, P = 0.03; males: 0.41 [0.06, 0.76], I2 = 0%, P = 0.89). VO2max had positive training effects when the HIIT recovery period had an interval time ≥2 min (0.44 [0.03, 0.84], I2 = 0%, P = 0.99) and recovery phase intensity ≤40% (0.38 [0.05, 0.71], I2 = 0%, P = 0.96). Thus, HIIT shows superiority over conventional training methods in improving VO2max, promoting aerobic capacity, in elite athletes.

Abstract The identification of subgroups of people living with HIV in China based on the severity of symptom clusters and individual symptoms is crucial to determine group-specific symptom management strategies. Participants reported 27 highly prevalent HIV/AIDS-related symptoms. Latent profile analysis based on symptom severity was used to identify person-centered subtypes of HIV/AIDS-related symptoms. Symptom networks were compared among subgroups identified by latent profile analysis. A total of 2927 eligible people living with HIV (PWH) were included in the analysis. Five profiles were identified: “Profile 1: all low symptom severity” (n2 = 2094, 71.54%), “Profile 2: medium symptom severity with syndemic conditions” (n3 = 109, 3.72%), “Profile 3: medium symptom severity with low functional status” (n1 = 165, 5.64%), “Profile 4: medium symptom severity in transitional period” (n4 = 448, 15.31%), and “Profile 5: all high symptom severity” (n5 = 111, 3.79%). Except for Profile 1 and Profile 5, the symptom severity was similar among the other three profiles. Profiles 1 (2.09 ± 0.52) and 4 (2.44 ± 0.66) had the smallest ∑s values, and Profiles 2 (4.38 ± 1.40) and 5 (4.39 ± 1.22) had the largest ∑s values. Our study demonstrates the need for health care professionals to provide PWH with group-specific symptom management interventions based on five profiles to improve their physical and psychological well-being. Future studies should be conducted in different contexts using different symptom checklists to further validate our results.

Abstract Rice bacterial blight, caused by Xanthomonas oryzae pv. oryzae (Xoo), is one of the most serious diseases affecting rice production worldwide. Xa21 was the first disease resistance gene cloned in rice, which encodes a receptor kinase and confers broad resistance against Xoo stains. Dozens of components in the Xa21-mediated pathway have been identified in the past decades, however, the involvement of mitogen-activated protein kinase (MAPK) genes in the pathway has not been well described. To identify MAPK involved in Xa21-mediated resistance, the level of MAPK proteins was profiled using Western blot analysis. The abundance of OsMPK17 (MPK17) was found decreased during the rice-Xoo interaction in the background of Xa21. To investigate the function of MPK17, MPK17-RNAi and over-expression (OX) transgenic lines were generated. The RNAi lines showed an enhanced resistance, while OX lines had impaired resistance against Xoo, indicating that MPK17 plays negative role in Xa21-mediated resistance. Furthermore, the abundance of transcription factor WRKY62 and pathogenesis-related proteins PR1A were changed in the MPK17 transgenic lines when inoculated with Xoo. We also observed that the MPK17-RNAi and -OX rice plants showed altered agronomic traits, indicating that MPK17 also plays roles in the growth and development. On the basis of the current study and published results, we propose a “Xa21-MPK17-WRKY62-PR1A” signaling that functions in the Xa21-mediated disease resistance pathway. The identification of MPK17 advances our understanding of the mechanism underlying Xa21-mediated immunity, specifically in the mid- and late-stages.

Twisted van der Waals structures represent a versatile platform to investigate topological and correlated electronic states. Here, the authors report the visualization of an electron crystal phase in twisted monolayer-bilayer graphene via scanning tunnelling microscopy, studying the coupling between strong electron correlation and nontrivial band topology.

Abstract Background The prevalence of comorbid depressive and anxiety disorders in people living with HIV (PLWH) is high. However, it is unclear which symptom is the bridge symptom between depression and anxiety in PLWH. This study aimed to develop symptom networks for depression and anxiety and explore the bridge symptoms and interconnectedness between these disorders in PLWH with comorbid depressive and anxiety disorders. Methods A multisite, hospital-based cross-sectional study was conducted from March 2020 to November 2021. Depression and anxiety were measured with the Hospital Anxiety and Depression Scale. We visualized the symptom network using the qgraph package and computed the bridge expected influence of each node. The GLASSO layout was used to generate undirected association networks. Results A total of 2016 individuals were included in the analysis. In the anxiety cluster, “not feeling relaxed” had the highest bridge expected influence and strength (r bridge expected influence = 0.628, r strength = 0.903). In the depression cluster, “not feeling cheerful” was identified as having a high bridge expected influence (r bridge expected influence = 0.385). “Not feeling cheerful” and “not feeling relaxed” were the strongest edges across the depression and anxiety clusters (r = 0.30 ± 0.02). Conclusions Healthcare professionals should take note when PLWH report severe bridge symptoms. To enhance the levels of perceived cheerfulness and relaxation, positive psychology interventions could be implemented.

Abstract Borderline personality disorder (BoPD or BPD) is highly prevalent and characterized by reactive moods, impulsivity, behavioral dysregulation, and distorted self-image. Yet the BoPD diagnosis is underutilized and patients with BoPD are frequently misdiagnosed resulting in lost opportunities for appropriate treatment. Automated screening of electronic health records (EHRs) is one potential strategy to help identify possible BoPD patients who are otherwise undiagnosed. We present the development and analytical validation of a BoPD screening algorithm based on routinely collected and structured EHRs. This algorithm integrates rule-based selection and machine learning (ML) in a two-step framework by first selecting potential patients based on the presence of comorbidities and characteristics commonly associated with BoPD, and then predicting whether the patients most likely have BoPD. Leveraging a large-scale US-based de-identified EHR database and our clinical expert’s rating of two random samples of patient EHRs, results show that our screening algorithm has a high consistency with our clinical expert’s ratings, with area under the receiver operating characteristic (AUROC) 0.837 [95% confidence interval (CI) 0.778–0.892], positive predictive value 0.717 (95% CI 0.583–0.836), accuracy 0.820 (95% CI 0.768–0.873), sensitivity 0.541 (95% CI 0.417–0.667) and specificity 0.922 (95% CI 0.880–0.960). Our aim is, to provide an additional resource to facilitate clinical decision making and promote the development of digital medicine.

Polarizers are an essential optical element for tailoring the polarization state of electromagnetic waves in a wide range of optical devices. Such polarizers, which exhibit a wide operating bandwidth and high performance, are attracting increasing attention, due to their extensive prospects for use in applications ranging from polarization imaging, to optical communications and detection, among others. However, achieving both broadband performance and ultra-high extinction ratio (ER), and that simultaneously, is still challenging in the design of effective polarizers. To tackle that demand, in this work, an Au-on-silica grating structure has been proposed as the basis of the design of a miniaturized high-efficiency polarizer that practically can cover the entire visible and near-infrared spectral ranges. The single-layer polarizer thus designed can show an ER of 60 dB in this spectral domain, and it has been shown that the geometrical parameters selected have a significant effect on the performance characteristics of the polarizer. Furthermore, an ER of ∼150 dB could be achieved merely by regulating the thickness of the grating to achieve the optimum performance. By integrating the high-performance polarizer proposed in this work with an optical fiber “meta-tip,” a refractive polarizer with a value of the ER of >45 dB, and that over the entire spectral domain considered, has been demonstrated. Such an approach offers an alternative route to achieving a broadband, powerful, and flexible processing polarizer design.

This paper introduces the harmonic requirements of a power system transformer and the principle of the Rogowski coil electronic current transformer, summarizes the simple and practical methods for harmonic measurement of the Rogowski coil electronic current transformer, and conducts harmonic measurement of the electronic current transformer through high-precision harmonic source and standard current transformer and transformer calibrator. The results show that this method has strong applicability and simple operation, which greatly enriches the harmonic measurement methods.